The ML Engineer’s life has become significantly easier over the past few years, but ML projects are still too tedious and complex. Feature stores have recently emerged as an important product category within the MLOps ecosystem. They solve part of …
Redis? Dynamo? Cassandra? How to choose the right online store for your ML features.
Where should you store your ML features to power real-time ML predictions, and why? In this talk, Tecton’s Co-Founder and CTO, Kevin Stumpf, will discuss the tradeoffs made and lessons learned while building the Feature Stores at Uber, Tecton and …
Using Feast in a Ranking System
This will be a practical session explaining how Better.com uses Feast in a Ranking System that depends on multiple data sources and several models. We’ll provide a walkthrough of several architectures we considered as a team to manage features, …
ML Projects Aren’t An Island
We’ve all seen the dismal and (at this point, annoying) charts and graphs of ‘>90.x% of ML projects fail’ used as marketing ploys by various companies. What this largely simplified view of ML project success rates buries in misleading abstraction …
Managing Data Infrastructure with Feast
Feast provides a simple framework for defining and serving machine learning features. In order to serve features reliably, with low latency and at high scale, Feast relies heavily on cloud infrastructure such as DynamoDB or AWS Lambda. This talk …
Building feature stores on Snowflake
Feature Stores cant exist without the underlying compute and storage platforms that they orchestrate. While there are many different options to choose from in the field today, there are several decisions that need to be made to ensure scalability, …
A Tour of Features in the Wild and a Modern Solution to Manage Them
Mike will kick off the event and present his views on the different types of features commonly used for Operational ML use cases, and solutions to manage them. Operational ML models rely on several types of features with different characteristics …
Using Redis as your Online Feature Store: 2021 highlights & 2022 directions
With the growing business demand for real-time predictions, we are witnessing companies making investments in modernizing their data architectures to support online inference. When companies need to deliver real-time ML applications to support …
High Performance Feature Serving with Feast on AWS
In this lightning talk we will showcase how teams can deploy and productionize a feature store on AWS with Feast. We will demonstrate how users can train a machine learning model using historical features from Redshift, deploy the model into …
How Robinhood Built a Feature Store Using Feast
Features are essential to ML models. Therefore, a good feature infrastructure is important to any organization that wants to use ML properly in production. Feast is a great tool for building up your feature infrastructure. However, using Feast in …
How Shopify Contributed to Scale Feast
This talk will discuss how Shopify manages large volumes of ML data (billions of rows) using Feast. Shopify decided to adopt Feast to build their ML Feature Store in early 2021. We will speak about how we contributed to Feast to make it more scalable …
